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Correlated Reference-Assisted Variational Quantum Eigensolver.

Nhan Trong Le1, Lan Nguyen Tran2,3

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Summary
This summary is machine-generated.

We introduce an active-space approximation to lower quantum resource needs for variational quantum eigensolver (VQE). This method significantly improves accuracy over standard VQE by incorporating electron correlation effects.

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Area of Science:

  • Quantum chemistry
  • Computational physics
  • Quantum computing

Background:

  • Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for finding molecular ground-state energies.
  • Current VQE methods often require significant quantum resources, limiting their applicability.
  • Active-space approximations aim to reduce computational cost by focusing on relevant electrons.

Purpose of the Study:

  • To develop a novel active-space approximation for VQE.
  • To reduce the quantum computational resources required for VQE calculations.
  • To improve the accuracy of VQE by including electron correlation effects.

Main Methods:

  • Utilized a double exponential unitary coupled-cluster ansatz and downfolding technique.
  • Derived an effective Hamiltonian incorporating a correlated potential from one-body second-order Møller-Plesset perturbation theory (OBMP2).
  • Employed canonical transformation and cumulant approximation for correlated potential calculation.

Main Results:

  • The proposed active-space approximation significantly reduces quantum resource requirements.
  • The method accurately predicts energies and density matrices (via dipole moment) for systems with singlet and doublet ground states.
  • Demonstrated superior performance compared to active-space VQE with a Hartree-Fock reference.

Conclusions:

  • The developed active-space approximation offers a more resource-efficient and accurate approach to VQE.
  • This method effectively captures electron correlation, enhancing VQE performance.
  • The approach shows promise for accurate quantum chemistry simulations on near-term quantum devices.